The Challenge in Evaluation of Nested Cohort Studies in Public Health and Occupational Health Settings

Dear Colleagues,

Studies in the fields of occupational medicine or public health are often conducted as multicenter studies. This results in a nested data structure. Hierarchically structured data are obtained when the study cohort encloses different groups (e.g., geographical or temporal distance from exposure). If the effects of exposure to pollutants/hazardous substances or effects of health interventions are defined as endpoints, it is often appropriate to repeat the measurements after time in order to capture a longitudinal course of the effect parameters. I think all of us have been confronted with the problem of correctly mapping such data in a statistical model. Unfortunately, we notice from time to time that researchers take the easy way out and evaluate such data in a cross-sectional view. The nested structure of the data is often neglected. Information contained in the data is not used, and an opportunity is missed. Modern statistics, however, offer a variety of possibilities to meet these challenges. By adapting the study design (crossover design, subtraction of repeated measurements, etc.), it is sometimes also possible to facilitate the correct handling of the data. The goal of this Special Issue is to provide examples from different research areas to illustrate the possibilities of analyzing and handling hierarchical, nested, and/or longitudinal data in the community and to contribute to the exchange of ideas.

I look forward to your contributions.

Kind regards

Dr. André Esser
Guest Editor

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